Increasing robustness of fault localization through analysis of lost, spurious, and positive symptoms

Abstract

This paper utilizes belief networks to implement fault localization in communication systems taking into account comprehensive information about the system behavior. Most previous work on this subject performs fault localization based solely on the information about malfunctioning system components (i.e., negative symptoms). We show that positive information, i.e., the lack of any disorder in some system components, may be used to improve the accuracy of this process. The technique presented allows lost and spurious symptoms to be incorporated in the analysis. We show through simulation that in a noisy network environment the analysis of lost and spurious symptoms increases the robustness of fault localization with belief networks. We also demonstrate that belief networks yield high accuracy even for approximate probability input data and therefore are a promising model for non-deterministic fault localization.